In practice, the prior parameters \(\sigma ^2\)  and \(\rho \)  are usually unknown and required to be estimated. Moreover, the true localization parameters \(\mathbf {\vartheta }\)  and \(\mathbf {\tau }\)  may not be the grid values illustrated in (REF ), and usually the values between adjacent grids resulting in the model mismatch problem. To address these issues, we treat \(\omega =\lbrace \sigma ^2,\rho ,\mathbf {\vartheta },\mathbf {\tau }\rbrace \)  as unknown parameters and utilize the EM approach [1]} to learn these parameters for improving the proposed algorithm. The EM process is described as
\(\omega (l+1)=\arg \max _{\omega }E_{p(\mathbf {\zeta }|\mathbf {Y};\omega (l))}\lbrace \ln p(\mathbf {Y},\mathbf {\zeta };\omega )\rbrace ,\) 
